Autonomous Engineering Infrastructure

Autonomous EngineeringInfrastructure.

AI editors generate syntax. AutoRail provides the execution infrastructure to test, verify, and reliably ship it — persistent context, behavioral parity, and self-healing so autonomous engineering works at enterprise scale.

The Problem

The 'Day 2' Hangover.

Autonomous engineering is happening — agents are shipping real code to real production systems. But there's no infrastructure underneath to catch what breaks:

session-log · agent-context
memory lost
S-001auth/login.tsforgot architectural conventions
S-002api/users.tsreinvented existing patterns
S-003core/payments.tsalien code merged to main
S-004lib/helpers.tsbroke naming conventions
S-005utils/format.tsduplicate utility created
S-006hooks/useAuth.tsignored team style guide
>
01

The Amnesia Collapse

every session starts from zero

Agents forget your architectural decisions. Every session is a clean slate. Patterns get reinvented. Conventions drift. Your developers spend more time fixing AI-generated code than they saved by using AI.

Solved by context layer
test-runner v2.1
4 passed1 failed
Syntax validation0.12s
Type checking0.34s
Unit tests (142/142)1.21s
Integration tests (28/28)3.04s
Behavioral parity
FAILBehavioral regression detected in checkout flow
02

The Verification Collapse

syntax passes, behavior breaks

Agents write code that passes syntax checks but breaks business behavior. Migrations look correct but feel wrong to users. You merge regressions faster than ever before.

Solved by migration layer
dependency-graph · live
drift detected
appauthapipaymentsdbcachebroken
03

Context Rot

dependency graph decaying

As the codebase grows, static rules files break down. Agents can't see the full system architecture. They hallucinate solutions that don't compose with anything around them.

Solved by knowledge graph

The Infrastructure

Two Layers. One Stack.

Autonomous engineering needs infrastructure underneath — or it collapses on Day 2.

unerr · CLI Sidecar

The Context Layer

Persistent memory infrastructure for agents. An AST-backed knowledge graph injected directly into your IDE agent via MCP — teaching it your patterns, conventions, and business intent across every session and every developer.

Explore unerr
unerr
1
# unerr runs alongside your IDE
2
$ unerr start --watch
3
4
■ MCP server running on stdio
5
■ Knowledge graph: 1,247 nodes
6
■ Watching commits for updates...
7
■ Skill libraries: 3 active
8
9
# Agents connect via MCP — zero config
10
✓ Cursor connected
11
✓ Pattern enforcement: ON
12
✓ Drift detection: ARMED
0%Less alien code
0xFaster onboarding
0Day-2 surprises
necroma · Web Portal

The Migration Layer

Autonomous legacy modernization. Records DOM events and user flows, generates Playwright tests from observed behavior, and forces the AI to write code until the tests pass. Not syntax translation — behavioral reconstruction.

Explore Necroma
necroma
1
> necroma scan --target legacy-auth
2
■ Recording DOM events + user flows...
3
■ Generating Playwright test suite...
4
■ 3 vertical slices identified
5
6
> necroma migrate --verify
7
✓ Behavioral tests generated
8
✓ Migrated: COBOL → TypeScript
9
✓ Guardrails: ARMED
0Modules scanned
0%Behavior preserved
Built On
MCP
CozoDB
LangGraph
TypeScript
OpenHands

Enterprise Infrastructure

Engineering Rigor for the Agentic Age.

AutoRail keeps the human in the loop as the reviewer and orchestrator. Agents propose. Your team approves.

change-ledger
rec
[14:32:01][cursor-agent]modified auth/login.ts
[14:32:03][unerr]pattern check: PASS
[14:32:04][cursor-agent]refactored /api/users.ts
[14:32:05][unerr]drift detected: WARNING
[14:32:07][cursor-agent]created tests/login.spec.ts
01

Audit Trails — The Change Ledger

Every autonomous decision is logged with full provenance. If the system fails, you have the black box — not a shrug and a "the AI did it."

data-flow · local
</>
Your Code
AutoRail
Cloud
Zero data leaves your perimeter
02

Privacy-First — Local Processing

Your proprietary architecture never leaves your perimeter. The infrastructure runs where your code lives. No data exfiltration. No third-party model training on your IP.

thought-signature · live
AI
cursor-agent
refactor: auth module
Confidence85%
40%
70%
85%
Session context matches 12 prior patterns in knowledge graph.
Behavioral tests cover 94% of affected endpoints.
Approved — ready for review
03

Explainability — Thought Signatures

See exactly why the agent made a decision — complete with confidence scores and logic trails — before a single line of code reaches production. No black-box deployments.

Built on the Agentic Stack
MCP
CozoDB
LangGraph
OpenHands
Playwright
Temporal
Supabase
TypeScript
AST
MCP
CozoDB
LangGraph
OpenHands
Playwright
Temporal
Supabase
TypeScript
AST
AST
TypeScript
Supabase
Temporal
Playwright
OpenHands
LangGraph
CozoDB
MCP
AST
TypeScript
Supabase
Temporal
Playwright
OpenHands
LangGraph
CozoDB
MCP

Powering the next generation of autonomous engineering

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autorail — Automated Code Review & Governance for AI Coding Tools